Improving Energy Efficiency with Dynamic Compiler-Directed Function Unit Power Control

2014 ◽  
Author(s):  
Yu Sun ◽  
Wei Zhang
Keyword(s):  
Energy Efficiency ◽  
Power Control ◽  
Unit Power ◽  
Function Unit

scholarly journals Joint UAV channel modeling and power control for 5G IoT networks

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Xiuhua Fu ◽  
Tian Ding ◽  
Rongqun Peng ◽  
Cong Liu ◽  
Mohamed Cheriet
Keyword(s):  
Energy Efficiency ◽  
Power Control ◽  
Channel Model ◽  
Path Loss ◽  
Channel Modeling ◽  
Signal Propagation ◽  
Spectrum Efficiency ◽  
Propagation Path ◽  
Control Factor ◽  
The Impact

AbstractThis paper studies the communication problem between UAVs and cellular base stations in a 5G IoT scenario where multiple UAVs work together. We are dedicated to the uplink channel modeling and the performance analysis of the uplink transmission. In the channel model, we consider the impact of 3D distance and multi-UAVs reflection on wireless signal propagation. The 3D distance is used to calculate the path loss, which can better reflect the actual path loss. The power control factor is used to adjust the UAV's uplink transmit power to compensate for different propagation path losses, so as to achieve precise power control. This paper proposes a binary exponential power control algorithm suitable for 5G networked UAV transmitters and presents the entire power control process including the open-loop phase and the closed-loop phase. The effects of power control factors on coverage probability, spectrum efficiency and energy efficiency under different 3D distances are simulated and analyzed. The results show that the optimal power control factor can be found from the point of view of energy efficiency.

scholarly journals Improving Energy Efficiency Fairness of Wireless Networks: A Deep Learning Approach

Energies ◽  
2019 ◽  
Vol 12 (22) ◽  
pp. 4300 ◽  
Author(s):  
Hoon Lee ◽  
Han Seung Jang ◽  
Bang Chul Jung
Keyword(s):  
Energy Efficiency ◽  
Wireless Networks ◽  
Deep Learning ◽  
Power Control ◽  
Control Method ◽  
Optimal Solution ◽  
Control Policy ◽  
Maximization Problem ◽  
Optimization Approach ◽  
Conventional Optimization

Achieving energy efficiency (EE) fairness among heterogeneous mobile devices will become a crucial issue in future wireless networks. This paper investigates a deep learning (DL) approach for improving EE fairness performance in interference channels (IFCs) where multiple transmitters simultaneously convey data to their corresponding receivers. To improve the EE fairness, we aim to maximize the minimum EE among multiple transmitter–receiver pairs by optimizing the transmit power levels. Due to fractional and max-min formulation, the problem is shown to be non-convex, and, thus, it is difficult to identify the optimal power control policy. Although the EE fairness maximization problem has been recently addressed by the successive convex approximation framework, it requires intensive computations for iterative optimizations and suffers from the sub-optimality incurred by the non-convexity. To tackle these issues, we propose a deep neural network (DNN) where the procedure of optimal solution calculation, which is unknown in general, is accurately approximated by well-designed DNNs. The target of the DNN is to yield an efficient power control solution for the EE fairness maximization problem by accepting the channel state information as an input feature. An unsupervised training algorithm is presented where the DNN learns an effective mapping from the channel to the EE maximizing power control strategy by itself. Numerical results demonstrate that the proposed DNN-based power control method performs better than a conventional optimization approach with much-reduced execution time. This work opens a new possibility of using DL as an alternative optimization tool for the EE maximizing design of the next-generation wireless networks.

Artificial Neural Network for PWM Rectifier Direct Power Control and DC Voltage Control

2018 ◽  
pp. 286-316 ◽  
Author(s):  
Arezki Fekik ◽  
Hakim Denoun ◽  
Ahmad Taher Azar ◽  
Mustapha Zaouia ◽  
Nabil Benyahia ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Power Control ◽  
Power Factor ◽  
Reactive Power ◽  
Pwm Rectifier ◽  
Direct Power ◽  
Direct Power Control ◽  
Dc Voltage ◽  
Unit Power

In this chapter, a new technique has been proposed for reducing the harmonic content of a three-phase PWM rectifier connected to the networks with a unit power factor and also providing decoupled control of the active and reactive instantaneous power. This technique called direct power control (DPC) is based on artificial neural network (ANN) controller, without line voltage sensors. The control technique is based on well-known direct torque control (DTC) ideas for the induction motor, which is applied to eliminate the harmonic of the line current and compensate for the reactive power. The main idea of this control is based on active and reactive power control loops. The DC voltage capacitor is regulated by the ANN controller to keep it constant and also provides a stable active power exchange. The simulation results are very satisfactory in the terms of stability and total harmonic distortion (THD) of the line current and the unit power factor.

scholarly journals Power Control Method for Energy Efficient Buffer-Aided Relay Systems

Energies ◽  
2019 ◽  
Vol 12 (17) ◽  
pp. 3234
Author(s):  
Jingon Joung ◽  
Han Lim Lee ◽  
Jian Zhao ◽  
Xin Kang
Keyword(s):  
Energy Efficiency ◽  
Power Control ◽  
Control Method ◽  
Relay Node ◽  
Destination Node ◽  
Transmit Power ◽  
Channel Condition ◽  
Relay Systems ◽  
Fixed Power ◽  
Fitting Model

In this paper, a power control method is proposed for a buffer-aided relay node (RN) to enhance the energy efficiency of the RN system. By virtue of a buffer, the RN can reserve the data at the buffer when the the channel gain between an RN and a destination node (DN) is weaker than that between SN and RN. The RN then opportunistically forward the reserved data in the buffer according to channel condition between the RN and the DN. By exploiting the buffer, RN reduces transmit power when it reduces the transmit data rate and reserve the data in the buffer. Therefore, without any total throughput reduction, the power consumption of RN can be reduced, resulting in the energy efficiency (EE) improvement of the RN system. Furthermore, for the power control, we devise a simple power control method based on a two-dimensional surface fitting model of an optimal transmit power of RN. The proposed RN power control method is readily and locally implementable at the RN, and it can significantly improve EE of the RN compared to the fixed power control method and the spectral efficiency based method as verified by the rigorous numerical results.

scholarly journals User Association and Power Control for Energy Efficiency Maximization in M2M-Enabled Uplink Heterogeneous Networks with NOMA

Sensors ◽  
2019 ◽  
Vol 19 (23) ◽  
pp. 5307 ◽  
Author(s):  
Shuang Zhang ◽  
Guixia Kang
Keyword(s):  
Energy Efficiency ◽  
Power Control ◽  
Heterogeneous Networks ◽  
Base Station ◽  
Sequential Optimization ◽  
User Association ◽  
Decode And Forward ◽  
Vast Number ◽  
Machine Type ◽  
Machine Type Communication

To support a vast number of devices with less energy consumption, we propose a new user association and power control scheme for machine to machine enabled heterogeneous networks with non-orthogonal multiple access (NOMA), where a mobile user (MU) acting as a machine-type communication gateway can decode and forward both the information of machine-type communication devices and its own data to the base station (BS) directly. MU association and power control are jointly considered in the formulated as optimization problem for energy efficiency (EE) maximization under the constraints of minimum data rate requirements of MUs. A many-to-one MU association matching algorithm is firstly proposed based on the theory of matching game. By taking swap matching operations among MUs, BSs, and sub-channels, the original problem can be solved by dealing with the EE maximization for each sub-channel. Then, two power control algorithms are proposed, where the tools of sequential optimization, fractional programming, and exhaustive search have been employed. Simulation results are provided to demonstrate the optimality properties of our algorithms under different parameter settings.

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